{"title":"Anton J. Haug","description":"\u003cp\u003eWelcome to the intriguing world of Anton J. Haug, a distinguished author in the realms of science and nature. Anton J. Haug's work provides invaluable insights into complex scientific concepts with clarity and precision. His dedication to making advanced topics accessible is evident in his compelling books, which are esteemed by both academics and enthusiasts.\u003c\/p\u003e\n\n\u003cp\u003eAmong his notable works is \u003cem\u003eBayesian Estimation and Tracking\u003c\/em\u003e, a comprehensive guide that delves into the sophisticated techniques of Bayesian methods, meticulously tailored for tracking and estimation tasks. This book is an excellent resource for anyone keen on understanding the practical applications of Bayesian statistics in real-world scenarios.\u003c\/p\u003e\n\n\u003cp\u003eAnton J. Haug's books are a treasure trove of knowledge for those interested in deepening their understanding of scientific phenomena. They serve as indispensable resources for students, professionals, and anyone with a passion for unraveling the intricacies of the natural world.\u003c\/p\u003e\n\n\u003cp\u003eExplore Anton J. Haug's collection and embark on a journey of discovery, where science and nature converge in a fascinating exploration of the universe.\u003c\/p\u003e","products":[{"product_id":"bayesian-estimation-and-tracking-by-anton-j-haug-9780470621707","title":"Bayesian Estimation and Tracking","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003e\u003cstrong\u003eA practical approach to estimating and tracking dynamic systems in real-world applications\u003c\/strong\u003e\u003c\/p\u003e\n\n\u003cp\u003eMuch of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. \u003cem\u003eBayesian Estimation and Tracking\u003c\/em\u003e addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noises.\u003c\/p\u003e\n\n\u003cp\u003eFeaturing a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of each estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand.\u003c\/p\u003e\n\n\u003cp\u003eCase studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB® toolbox of estimation methods.\u003c\/p\u003e\n\n\u003cp\u003e\u003cem\u003eBayesian Estimation and Tracking\u003c\/em\u003e is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Unknown","offers":[{"title":"Default Title","offer_id":46854032720108,"sku":"9780470621707","price":280.99,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/14981623482355.jpg?v=1759232047"}],"url":"https:\/\/bookhero.co.nz\/collections\/anton-j-haug.oembed","provider":"Book Hero","version":"1.0","type":"link"}